{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "# 减小训练的规模\n",
    "\n",
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "**Note: For the three distance computations that we require you to implement in this notebook, you may not use the np.linalg.norm() function that numpy provides.**\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3803.92350081 4210.59603857 5504.0544147  ... 4007.64756434\n",
      "  4203.28086142 4354.20256764]\n",
      " [6336.83367306 5270.28006846 4040.63608854 ... 4829.15334194\n",
      "  4694.09767687 7768.33347636]\n",
      " [5224.83913628 4250.64289255 3773.94581307 ... 3766.81549853\n",
      "  4464.99921613 6353.57190878]\n",
      " ...\n",
      " [5366.93534524 5062.8772452  6361.85774755 ... 5126.56824786\n",
      "  4537.30613911 5920.94156364]\n",
      " [3671.92919322 3858.60765044 4846.88157479 ... 3521.04515734\n",
      "  3182.3673578  4448.65305458]\n",
      " [6960.92443573 6083.71366848 6338.13442584 ... 6083.55504619\n",
      "  4128.24744898 8041.05223214]]\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1** \n",
    "\n",
    "Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 139 / 500 correct => accuracy: 0.278000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "# **Inline Question 2**\n",
    "\n",
    "We can also use other distance metrics such as L1 distance.\n",
    "For pixel values $p_{ij}^{(k)}$ at location $(i,j)$ of some image $I_k$, \n",
    "\n",
    "the mean $\\mu$ across all pixels over all images is $$\\mu=\\frac{1}{nhw}\\sum_{k=1}^n\\sum_{i=1}^{h}\\sum_{j=1}^{w}p_{ij}^{(k)}$$\n",
    "And the pixel-wise mean $\\mu_{ij}$ across all images is \n",
    "$$\\mu_{ij}=\\frac{1}{n}\\sum_{k=1}^np_{ij}^{(k)}.$$\n",
    "The general standard deviation $\\sigma$ and pixel-wise standard deviation $\\sigma_{ij}$ is defined similarly.\n",
    "\n",
    "Which of the following preprocessing steps will not change the performance of a Nearest Neighbor classifier that uses L1 distance? Select all that apply.\n",
    "1. Subtracting the mean $\\mu$ ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu$.)\n",
    "2. Subtracting the per pixel mean $\\mu_{ij}$  ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu_{ij}$.)\n",
    "3. Subtracting the mean $\\mu$ and dividing by the standard deviation $\\sigma$.\n",
    "4. Subtracting the pixel-wise mean $\\mu_{ij}$ and dividing by the pixel-wise standard deviation $\\sigma_{ij}$.\n",
    "5. Rotating the coordinate axes of the data.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('One loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true,
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('No loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 3072)\n",
      "(500, 3072)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(500,)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_test.shape)\n",
    "a=X_test**2\n",
    "print(a.shape)\n",
    "b=np.sum(X_test**2,axis=1)\n",
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "no_loop",
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 29.673532 seconds\n",
      "One loop version took 67.476561 seconds\n",
      "No loop version took 0.225396 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# You should see significantly faster performance with the fully vectorized implementation!\n",
    "\n",
    "# NOTE: depending on what machine you're using, \n",
    "# you might not see a speedup when you go from two loops to one loop, \n",
    "# and might even see a slow-down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 3072)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(8.0)\n",
    "#np.reshape(XTrain,(4000,3072))\n",
    "XTrain.shape=(4000,3072)\n",
    "XTrain.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "[9 4 3 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 3 8 4]\n",
      "[9 4 3 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 3 8 4]\n",
      "[9 4 3 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 3 8 4]\n",
      "[9 4 3 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 3 8 4]\n",
      "[9 4 3 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 3 8 4]\n",
      "[9 4 3 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 3 8 4]\n",
      "[9 4 3 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 3 8 4]\n",
      "[9 4 3 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 3 8 4]\n",
      "[9 4 3 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 3 8 4]\n",
      "[9 4 3 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 5 4 6]\n",
      "[6 9 9 ... 3 8 4]\n",
      "k = 1, accuracy = 0.263000\n",
      "k = 1, accuracy = 0.257000\n",
      "k = 1, accuracy = 0.264000\n",
      "k = 1, accuracy = 0.278000\n",
      "k = 1, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.239000\n",
      "k = 3, accuracy = 0.249000\n",
      "k = 3, accuracy = 0.240000\n",
      "k = 3, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.254000\n",
      "k = 5, accuracy = 0.248000\n",
      "k = 5, accuracy = 0.266000\n",
      "k = 5, accuracy = 0.280000\n",
      "k = 5, accuracy = 0.292000\n",
      "k = 5, accuracy = 0.280000\n",
      "k = 8, accuracy = 0.262000\n",
      "k = 8, accuracy = 0.282000\n",
      "k = 8, accuracy = 0.273000\n",
      "k = 8, accuracy = 0.290000\n",
      "k = 8, accuracy = 0.273000\n",
      "k = 10, accuracy = 0.265000\n",
      "k = 10, accuracy = 0.296000\n",
      "k = 10, accuracy = 0.276000\n",
      "k = 10, accuracy = 0.284000\n",
      "k = 10, accuracy = 0.280000\n",
      "k = 12, accuracy = 0.260000\n",
      "k = 12, accuracy = 0.295000\n",
      "k = 12, accuracy = 0.279000\n",
      "k = 12, accuracy = 0.283000\n",
      "k = 12, accuracy = 0.280000\n",
      "k = 15, accuracy = 0.252000\n",
      "k = 15, accuracy = 0.289000\n",
      "k = 15, accuracy = 0.278000\n",
      "k = 15, accuracy = 0.282000\n",
      "k = 15, accuracy = 0.274000\n",
      "k = 20, accuracy = 0.270000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.282000\n",
      "k = 20, accuracy = 0.285000\n",
      "k = 50, accuracy = 0.271000\n",
      "k = 50, accuracy = 0.288000\n",
      "k = 50, accuracy = 0.278000\n",
      "k = 50, accuracy = 0.269000\n",
      "k = 50, accuracy = 0.266000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.270000\n",
      "k = 100, accuracy = 0.263000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.263000\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****z\n",
    "X_train_folds=np.array_split(X_train,num_folds)\n",
    "y_train_folds=np.array_split(y_train,num_folds)\n",
    "pass\n",
    "print(type(X_train_folds[0]))\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "for k in k_choices:\n",
    "    accuarcies=[];\n",
    "    for i in range(num_folds):\n",
    "        XTrain=[]\n",
    "        YTrain=[];\n",
    "        for j in range(num_folds):\n",
    "            if j!=i:\n",
    "                XTrain.append(X_train_folds[j])\n",
    "                YTrain.append(y_train_folds[j])\n",
    "        XTrain=np.array(XTrain)\n",
    "        YTrain=np.array(YTrain)\n",
    "        XTrain.shape=(XTrain.shape[0]*XTrain.shape[1],XTrain.shape[2])\n",
    "        YTrain.shape=(YTrain.shape[0]*YTrain.shape[1])\n",
    "        #print(YTrain.shape)\n",
    "        XTest=X_train_folds[i]\n",
    "        YTest=y_train_folds[i]\n",
    "        classifier.train(XTrain,YTrain)\n",
    "        dists=classifier.compute_distances_no_loops(XTest)\n",
    "        YPred=classifier.predict_labels(dists,k)\n",
    "        print(YTrain)\n",
    "        accuarcy=float(np.sum(YPred==YTest)/YPred.shape[0])\n",
    "        accuarcies.append(accuarcy)\n",
    "    k_to_accuracies[k]=accuarcies\n",
    "pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "id": "cross_validation"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 141 / 500 correct => accuracy: 0.282000\n"
     ]
    }
   ],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k =10\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 3**\n",
    "\n",
    "Which of the following statements about $k$-Nearest Neighbor ($k$-NN) are true in a classification setting, and for all $k$? Select all that apply.\n",
    "1. The decision boundary of the k-NN classifier is linear.\n",
    "2. The training error of a 1-NN will always be lower than that of 5-NN.\n",
    "3. The test error of a 1-NN will always be lower than that of a 5-NN.\n",
    "4. The time needed to classify a test example with the k-NN classifier grows with the size of the training set.\n",
    "5. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "\n"
   ]
  }
 ],
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